diff rcve.py @ 0:d8c414b9d774 draft default tip

Imported from capsule None

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/rcve.py	Tue Apr 01 09:13:06 2014 -0400
@@ -0,0 +1,142 @@
+#!/usr/bin/env python
+
+import sys
+from rpy import *
+import numpy
+
+def stop_err(msg):
+    sys.stderr.write(msg)
+    sys.exit()
+
+
+def sscombs(s):
+    if len(s) == 1:
+        return [s]
+    else:
+        ssc = sscombs(s[1:])
+        return [s[0]] + [s[0]+comb for comb in ssc] + ssc
+
+
+infile = sys.argv[1]
+y_col = int(sys.argv[2])-1
+x_cols = sys.argv[3].split(',')
+outfile = sys.argv[4]
+
+print "Predictor columns: %s; Response column: %d" % ( x_cols, y_col+1 )
+fout = open(outfile,'w')
+
+for i, line in enumerate( file ( infile )):
+    line = line.rstrip('\r\n')
+    if len( line )>0 and not line.startswith( '#' ):
+        elems = line.split( '\t' )
+        break
+    if i == 30:
+        break # Hopefully we'll never get here...
+
+if len( elems )<1:
+    stop_err( "The data in your input dataset is either missing or not formatted properly." )
+
+y_vals = []
+x_vals = []
+
+for k, col in enumerate(x_cols):
+    x_cols[k] = int(col)-1
+    x_vals.append([])
+    """
+    try:
+        float( elems[x_cols[k]] )
+    except:
+        try:
+            msg = "This operation cannot be performed on non-numeric column %d containing value '%s'." % ( col, elems[x_cols[k]] )
+        except:
+            msg = "This operation cannot be performed on non-numeric data."
+        stop_err( msg )
+    """
+NA = 'NA'
+for ind, line in enumerate( file( infile )):
+    if line and not line.startswith( '#' ):
+        try:
+            fields = line.split("\t")
+            try:
+                yval = float(fields[y_col])
+            except Exception, ey:
+                yval = r('NA')
+                #print >>sys.stderr, "ey = %s" %ey
+            y_vals.append(yval)
+            for k, col in enumerate(x_cols):
+                try:
+                    xval = float(fields[col])
+                except Exception, ex:
+                    xval = r('NA')
+                    #print >>sys.stderr, "ex = %s" %ex
+                x_vals[k].append(xval)
+        except:
+            pass
+
+x_vals1 = numpy.asarray(x_vals).transpose()
+dat = r.list( x=array(x_vals1), y=y_vals )
+
+set_default_mode(NO_CONVERSION)
+try:
+    full = r.lm( r("y ~ x"), data=r.na_exclude(dat) )  #full model includes all the predictor variables specified by the user
+except RException, rex:
+    stop_err("Error performing linear regression on the input data.\nEither the response column or one of the predictor columns contain no numeric values.")
+set_default_mode(BASIC_CONVERSION)
+
+summary = r.summary(full)
+fullr2 = summary.get('r.squared','NA')
+
+if fullr2 == 'NA':
+    stop_err("Error in linear regression")
+
+if len(x_vals) < 10:
+    s = ""
+    for ch in range(len(x_vals)):
+        s += str(ch)
+else:
+    stop_err("This tool only works with less than 10 predictors.")
+
+print >> fout, "#Model\tR-sq\tRCVE_Terms\tRCVE_Value"
+all_combos = sorted(sscombs(s), key=len)
+all_combos.reverse()
+for j, cols in enumerate(all_combos):
+    #if len(cols) == len(s):    #Same as the full model above
+    #    continue
+    if len(cols) == 1:
+        x_vals1 = x_vals[int(cols)]
+    else:
+        x_v = []
+        for col in cols:
+            x_v.append(x_vals[int(col)])
+        x_vals1 = numpy.asarray(x_v).transpose()
+    dat = r.list(x=array(x_vals1), y=y_vals)
+    set_default_mode(NO_CONVERSION)
+    red = r.lm(r("y ~ x"), data= dat)    #Reduced model
+    set_default_mode(BASIC_CONVERSION)
+    summary = r.summary(red)
+    redr2 = summary.get('r.squared','NA')
+    try:
+        rcve = (float(fullr2)-float(redr2))/float(fullr2)
+    except:
+        rcve = 'NA'
+    col_str = ""
+    for col in cols:
+        col_str = col_str + str(int(x_cols[int(col)]) + 1) + " "
+    col_str.strip()
+    rcve_col_str = ""
+    for col in s:
+        if col not in cols:
+            rcve_col_str = rcve_col_str + str(int(x_cols[int(col)]) + 1) + " "
+    rcve_col_str.strip()
+    if len(cols) == len(s):    #full model
+        rcve_col_str = "-"
+        rcve = "-"
+    try:
+        redr2 = "%.4f" % (float(redr2))
+    except:
+        pass
+    try:
+        rcve = "%.4f" % (float(rcve))
+    except:
+        pass
+    print >> fout, "%s\t%s\t%s\t%s" % ( col_str, redr2, rcve_col_str, rcve )